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### Data Analysis and Statistical Modeling in R

Learn the foundation of Data Science, Analytics and Data interpretation using statistical tests with real world examples

### What you'll learn?

- Develop and execute Hypothesis 1-tailed and 2-tailed tests in R
- Understand statistical Data Distributions and their functions in R
- To pick suitable visualisation technique according to problem
- Test differences, durability and data limitations
- How to interpret different output values and make conclusions
- Statistical modelling in R with real world examples and datasets
- R packages which can improve statistical modelling
- Custom Data visualisations using R with limitations and interpretation
- To pick suitable statistical technique according to problem
- Applications of Statistical tests

### Requirements and What you should know?

- Should know basic R fundamentals such as vectors, data frames etc.
- Students should know and familiar with MAC/Linux distribution software installation, if they are using one.
- Course will teach how to install R and R-studio on Windows OS

### Who is this course for?

- University and college data science students
- Data Science aspirants
- people who want to shift from SPSS and EXCEL to R to perform statistical analysis
- Beginners who want to perform statistical modelling and learn about its applications

### What is this course about?

Before applying any data science model its always a good practice to understand the true nature of your data. In this Course we will cover fundamentals and applications of statistical modelling. We will use R Programming Language to run this analysis. We will start with Math, Data Distribution and statistical concepts then by using plots and charts we will interpret our data. We will use statistical modelling to prove our claims and use hypothesis testing to confidently make inferences.

This course is divided into 3 Parts

In the 1st section we will cover following concepts

1. Normal Distribution

2. Binomial Distribution

3. Chi-Square Distribution

4. Densities

5. Cumulative Distribution function CDF

6. Quantiles

7. Random Numbers

8. Central Limit Theorem CLT

9. R Statistical Distribution

10. Distribution Functions

11. Mean

12. Median

13. Range

14. Standard deviation

15. Variance

16. Sum of squares

17. Skewness

18. Kurtosis

2nd Section

1. Bar Plots

2. Histogram

3. Pie charts

4. Box plots

5. Scatter plots

6. Dot Charts

7. Mat Plots

8. Plots for groups

9. Plotting datasets

3rd Section of this course will elaborate following concepts

1. Parametric tests

2. Non-Parametric Tests

3. What is statistically significant means?

4. P-Value

5. Hypothesis Testing

6. Two-Tailed Test

7. One Tailed Test

8. True Population mean

9. Hypothesis Testing

10. Proportional Test

11. T-test

12. Default t-test / One sample t-test

13. Two-sample t-test / Independent Samples t-test

14. Paired sample t-test

15. F-Tests

16. Mean Square Error MSE

17. F-Distribution

18. Variance

19. Sum of squares

20. ANOVA Table

21. Post-hoc test

22. Tukey HSD

23. Chi-Square Tests

24. One sample chi-square goodness of fit test

25. chi-square test for independence

26. Correlation

27. Pearson Correlation

28. Spearman Correlation

In all the analysis we will practically see the real world applications using data sets csv files and r built in Datasets and packages.

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